Faculty Profile

Mark Albert

Title
Assistant Professor
Department
Computer Science and Engineering
College
College of Engineering

    

Education

PhD, Cornell University, 2010.
Major: Computational Biology
Dissertation Title: Normative Visual Development: innate learning in the early visual system
Carnegie Mellon University, 2004.
Major: Computational Neuroscience
University of Pittsburgh-Pittsburgh Campus, 2004.
Major: Computational Neuroscience
Universität Wien, 2001.
Major: AI-Related Coursework
BS, Pittsburg State University, 2000.
Major: Computer Science, Chemistry, Physics, and Mathematics

Current Scheduled Teaching*

BMEN 5280.001, AI for Wearables and Healthcare, Fall 2020
CSCE 5280.001, AI for Wearables and Healthcare, Fall 2020
INFO 6950.709, Doctoral Dissertation, Fall 2020
CSCE 6940.712, Individual Research, Fall 2020
CSCE 5950.712, Master's Thesis, Fall 2020
CSCE 5214.001, Software Development for Artificial Intelligence, Fall 2020
CSCE 5214.004, Software Development for Artificial Intelligence, Fall 2020
CSCE 5214.008, Software Development for Artificial Intelligence, Fall 2020
CSCE 2900.712, Special Problems in Computer Science and Engineering, Fall 2020

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

CSCE 2999Z.712, CSCE Research, Summer 10W 2020
CSCE 5934.712, Directed Study, Spring 2020
CSCE 6940.712, Individual Research, Spring 2020
INFO 6660.012, Readings in Information Science, Spring 2020
CSCE 4930.712, Topics in Computer Science and Engineering, Spring 2020 Syllabus
CSCE 5933.712, Topics in Computer Science and Engineering, Spring 2020 Syllabus
CSCE 4890.712, Directed Study, Fall 2019
CSCE 5934.712, Directed Study, Fall 2019
CSCE 3996.002, Honors College Mentored Research Experience, Fall 2019
CSCE 6940.712, Individual Research, Fall 8W2 2019
CSCE 6940.712, Individual Research, Fall 2019
CSCE 4205.001, Introduction to Machine Learning, Fall 2019 Syllabus SPOT
CSCE 5215.001, Machine Learning, Fall 2019 SPOT
CSCE 5215.600, Machine Learning, Fall 2019 SPOT

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

,
Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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